neural models learn the relationships between input observations and output variables directly from data. Neural networks can run in a matter of seconds on parallel hardware and can thus generate forecasts more frequently and with higher spatial resolution. The networks are also notably simple and can be specified with generic modules in a few dozens of lines of code without hand tuned routines for a specific task. These properties can not only offer improved forecasts, but also frequent and personalized forecasts [1]} and open avenues of new applications that rely on the models' efficiency and flexibility. However, showing that the neural networks are able to learn to emulate the physics of the atmosphere sufficiently well to make skillful high-resolution forecasts for up to twelve hours ahead–a period that requires an advanced understanding of atmospheric physics and is well beyond the skill of extrapolation and short-term nowcasting methods [2]}, [3]}, [4]}, [5]}–is a substantial open challenge at the core of our new modelling paradigm.
